It might be hard to take the thought seriously when a satnav sends you down a dead-end country road, or your phone’s autocorrect feature turns a carefully-constructed text message to gibberish, but the milestones reached in the last year alone have been exceptional.

DeepMind, the British AI company owned by Google, has defeated the world champion at Go, the ancient game that requires a finely-tuned sense of intuition to master. Driverless cars now seem like an inevitability rather than a curiosity. Error rates on image recognition technology have dropped from 25pc in 2011 to less than 4pc.

AI is graduating from theory and academic papers to everyday life. If the last 10 years has been defined by the plummeting costs of microprocessors and sensors that have made smartphones a commodity product, the future is about building intelligent systems that can make them more powerful.

Ergo, the companies that will profit might not be the ones with expertise in hardware design, but those who can build software that talks back.

Google, always a company defined by the effectiveness of its algorithms over others, has gone 'all in’ on deep learning, and has incorporated it into thousands of software projects.

Mark Zuckerberg, determined not to miss out on any trend after Facebook’s early failures in mobile, has poached some of the world’s leading AI gurus from universities.

Amazon has emerged as a sleeping giant in the field. And Microsoft is ploughing resources into artificial intelligence, albeit with mixed success (a conversational Twitter bot it unveiled earlier this year was swiftly shut down after “learning” to spew vile insults at those who engaged with it).

The missing name here is Apple. The undisputed victor of the smartphone-building wars, in profit and influence if not quite in market share, Apple’s expertise when it comes to AI is less clear.

This is partly because it has had less incentive – Apple is not in the business of mining data to target adverts, at least not as deeply as Google and Facebook, and is still a hardware company at heart.

Any breakthroughs that do happen at Apple HQ, meanwhile, are kept quiet, unlike those of other major technology companies.

This means both that we don’t know about them, but also that the academic computer scientists making the running on AI research can be somewhat reluctant to join a company at which they won’t be able to trumpet their achievements.

But another reason, and one that is often brushed aside by other tech groups invested in AI, is privacy. Artificial intelligence, at least for now, needs to be trained on heaps of data.

A computer vision programme does not instinctively know what a cat is – it must be shown millions of photos of cats to be able to identify one, and even then, tends to recognise cat characteristics – four legs, tail – rather than the understanding a human will have (show it a cat with three legs and it might struggle).

Apple’s uncompromising stance on privacy – culminated in Tim Cook’s very public battle with the FBI over the company’s refusal to unlock a terrorist’s iPhone this year, as well as vocal opposition to the British Government’s Investigatory Powers Bill – means it takes great pains not collect the data that Google and Facebook do.

Much of the personal information on an iPhone never leaves the device, and the data that does go online is strictly encrypted.

While this has won Apple plaudits, it could also threaten to hold it back in the AI race. By coming down on the side of privacy in the inherent trade-off with progress, Apple’s ability to create the services of the future could suffer.

Take Allo, a new messaging app from Google. By reading a user’s conversations, Allo is able to observe their writing style, and after some time, learn to write messages for them, suggesting potential replies based on previous ones. The trade-off is that end-to-end encryption, the security protocol that means only the sender and recipient can read a message, is disabled on Allo.

Apple’s messaging app, meanwhile, is fully encrypted, making personalised text technology a bigger challenge, and the data collected by Siri, its AI assistant software, never leaves a phone, so is never stored on Apple's servers for processing.

At the start of this week, at its annual software conference – the most important one for years, given that the company’s revenue is falling for the first time in a decade – Apple attempted to prove that it doesn’t have to sacrifice privacy for progress. It announced that it would be adopting a technique called “differential privacy”, which scrambles users’ data using statistical noise, essentially anonymising it.

The company says the technique will enable it to study patterns of large numbers of users without affecting their privacy. Lukasz Olejnik, a security and privacy consultant and University College London researcher, calls Apple’s introduction of differential privacy “an impressive milestone for privacy engineering” that is “clearly a step in the right direction”.

The announcement this week came as Cook and his lieutenants unveiled a string of software inventions that will require better knowledge of its users to reach their full potential. It showed off a souped-up version of Siri that can order taxis or send a friend money, a much-needed upgrade for a virtual assistant that was impressive when Apple unveiled it in 2011 but has been left behind by younger and smarter versions from Amazon and Google.

Improved computer vision technology in its photos app will let an iPhone put together slideshows of holidays or birthdays in the same way that we once curated photo albums – a feature that Google has pushed in its own photos as an example of the internet company’s AI prowess.

Apple’s clear advantage in hardware has not diminished, but the battleground has shifted. As online services powered by AI become our primary way of interacting with our computers, it is facing new direct competitors, most of which are not quite as principled about privacy.

Apple’s stance on protecting its users’ data is acute – we are becoming more savvy about what our internet overlords know. But if that data is the key to the next computing age, it will have to navigate a tricky line to avoid being left behind.